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1
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energies (Basel), 2019-04, Vol.12 (7), p.1301 [Peer Reviewed Journal]

2019. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en12071301

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2
A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm
A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm
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A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm

Energies (Basel), 2019-02, Vol.12 (4), p.660 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en12040660

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3
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand
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An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Energies (Basel), 2021-02, Vol.14 (3), p.569 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14030569

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4
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
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A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Energies (Basel), 2021-08, Vol.14 (16), p.5196 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14165196

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5
Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost
Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost
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Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost

Energies (Basel), 2023-05, Vol.16 (10), p.4159 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16104159

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6
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Air Temperature Forecasting Using Machine Learning Techniques: A Review
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Air Temperature Forecasting Using Machine Learning Techniques: A Review

Energies (Basel), 2020-08, Vol.13 (16), p.4215 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13164215

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7
Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

Energies (Basel), 2022-06, Vol.15 (11), p.3958 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15113958

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8
Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation
Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation
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Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

Energies (Basel), 2021-09, Vol.14 (18), p.5657 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14185657

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9
A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power
A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power
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A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power

Energies (Basel), 2020-12, Vol.13 (24), p.6623 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13246623

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10
Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review
Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review
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Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Energies (Basel), 2021-08, Vol.14 (16), p.5150 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14165150

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11
A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning
A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning
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A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Energies (Basel), 2021-09, Vol.14 (17), p.5322 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14175322

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12
Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
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Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Energies (Basel), 2020-01, Vol.13 (1), p.147 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13010147

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13
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine
Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine
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Identification of Power Transformer Winding Mechanical Fault Types Based on Online IFRA by Support Vector Machine

Energies (Basel), 2017-12, Vol.10 (12), p.2022 [Peer Reviewed Journal]

Copyright MDPI AG 2017 ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en10122022

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14
Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning
Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning
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Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Energies (Basel), 2020-10, Vol.13 (19), p.5190 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13195190

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15
Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks
Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks
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Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

Energies (Basel), 2020-09, Vol.13 (18), p.4881 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13184881

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16
Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews
Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews
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Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews

Energies (Basel), 2020-07, Vol.13 (13), p.3460 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13133460

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17
Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls
Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls
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Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls

Energies (Basel), 2021-05, Vol.14 (10), p.2777 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14102777

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18
Machine Learning for Short-Term Load Forecasting in Smart Grids
Machine Learning for Short-Term Load Forecasting in Smart Grids
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Machine Learning for Short-Term Load Forecasting in Smart Grids

Energies (Basel), 2022-11, Vol.15 (21), p.8079 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15218079

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19
Machine Learning Schemes for Anomaly Detection in Solar Power Plants
Machine Learning Schemes for Anomaly Detection in Solar Power Plants
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Machine Learning Schemes for Anomaly Detection in Solar Power Plants

Energies (Basel), 2022-02, Vol.15 (3), p.1082 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15031082

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20
Big Data for Energy Management and Energy-Efficient Buildings
Big Data for Energy Management and Energy-Efficient Buildings
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Big Data for Energy Management and Energy-Efficient Buildings

Energies (Basel), 2020, Vol.13 (7), p.1555 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13071555

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